Overview

Dataset statistics

Number of variables22
Number of observations7857
Missing cells4261
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory176.0 B

Variable types

Numeric20
Categorical2

Alerts

IMPOSTO SOBRE IMPORTAÇÃO has 281 (3.6%) missing valuesMissing
IPI - FUMO has 292 (3.7%) missing valuesMissing
IPI - BEBIDAS has 292 (3.7%) missing valuesMissing
IPI - AUTOMÓVEIS has 115 (1.5%) missing valuesMissing
IPI - VINCULADO À IMPORTACAO has 239 (3.0%) missing valuesMissing
IPI - OUTROS has 261 (3.3%) missing valuesMissing
IRPF has 304 (3.9%) missing valuesMissing
IRPJ - ENTIDADES FINANCEIRAS has 147 (1.9%) missing valuesMissing
IRPJ - DEMAIS EMPRESAS has 381 (4.8%) missing valuesMissing
IRRF - RENDIMENTOS DO TRABALHO has 375 (4.8%) missing valuesMissing
IRRF - RENDIMENTOS DO CAPITAL has 216 (2.7%) missing valuesMissing
IRRF - REMESSAS P/ EXTERIOR has 183 (2.3%) missing valuesMissing
IRRF - OUTROS RENDIMENTOS has 217 (2.8%) missing valuesMissing
IMPOSTO S/ OPERAÇÕES FINANCEIRAS has 162 (2.1%) missing valuesMissing
CPMF has 238 (3.0%) missing valuesMissing
COFINS has 452 (5.8%) missing valuesMissing
IMPOSTO SOBRE EXPORTAÇÃO is highly skewed (γ1 = 45.1450769)Skewed
UF is uniformly distributedUniform
IMPOSTO SOBRE EXPORTAÇÃO has 912 (11.6%) zerosZeros
IPI - FUMO has 2944 (37.5%) zerosZeros
IPI - BEBIDAS has 664 (8.5%) zerosZeros
IPI - AUTOMÓVEIS has 3309 (42.1%) zerosZeros
IPI - VINCULADO À IMPORTACAO has 542 (6.9%) zerosZeros
IRPJ - ENTIDADES FINANCEIRAS has 655 (8.3%) zerosZeros
IRRF - REMESSAS P/ EXTERIOR has 205 (2.6%) zerosZeros
CPMF has 3917 (49.9%) zerosZeros
COFINS has 6541 (83.3%) zerosZeros

Reproduction

Analysis started2024-06-04 00:58:50.621307
Analysis finished2024-06-04 00:59:11.236248
Duration20.61 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Ano
Real number (ℝ)

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.6289
Minimum2000
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.5 KiB
2024-06-03T21:59:11.275823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001
Q12006
median2012
Q32018
95-th percentile2023
Maximum2024
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.0016525
Coefficient of variation (CV)0.0034805886
Kurtosis-1.1991098
Mean2011.6289
Median Absolute Deviation (MAD)6
Skewness0.0034488375
Sum15805368
Variance49.023138
MonotonicityIncreasing
2024-06-03T21:59:11.327268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2000 324
 
4.1%
2013 324
 
4.1%
2023 324
 
4.1%
2022 324
 
4.1%
2021 324
 
4.1%
2020 324
 
4.1%
2019 324
 
4.1%
2018 324
 
4.1%
2017 324
 
4.1%
2016 324
 
4.1%
Other values (15) 4617
58.8%
ValueCountFrequency (%)
2000 324
4.1%
2001 324
4.1%
2002 324
4.1%
2003 324
4.1%
2004 324
4.1%
2005 324
4.1%
2006 324
4.1%
2007 324
4.1%
2008 324
4.1%
2009 324
4.1%
ValueCountFrequency (%)
2024 81
 
1.0%
2023 324
4.1%
2022 324
4.1%
2021 324
4.1%
2020 324
4.1%
2019 324
4.1%
2018 324
4.1%
2017 324
4.1%
2016 324
4.1%
2015 324
4.1%

Mês
Categorical

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size61.5 KiB
Janeiro
675 
Fevereiro
675 
Março
675 
Abril
648 
Maio
648 
Other values (7)
4536 

Length

Max length9
Median length8
Mean length6.4226804
Min length4

Characters and Unicode

Total characters50463
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJaneiro
2nd rowJaneiro
3rd rowJaneiro
4th rowJaneiro
5th rowJaneiro

Common Values

ValueCountFrequency (%)
Janeiro 675
8.6%
Fevereiro 675
8.6%
Março 675
8.6%
Abril 648
8.2%
Maio 648
8.2%
Junho 648
8.2%
Julho 648
8.2%
Agosto 648
8.2%
Setembro 648
8.2%
Outubro 648
8.2%
Other values (2) 1296
16.5%

Length

2024-06-03T21:59:11.377536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
janeiro 675
8.6%
fevereiro 675
8.6%
março 675
8.6%
abril 648
8.2%
maio 648
8.2%
junho 648
8.2%
julho 648
8.2%
agosto 648
8.2%
setembro 648
8.2%
outubro 648
8.2%
Other values (2) 1296
16.5%

Most occurring characters

ValueCountFrequency (%)
o 8505
16.9%
e 5940
11.8%
r 5940
11.8%
b 3240
 
6.4%
i 2646
 
5.2%
u 2592
 
5.1%
a 1998
 
4.0%
J 1971
 
3.9%
t 1944
 
3.9%
m 1944
 
3.9%
Other values (15) 13743
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50463
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 8505
16.9%
e 5940
11.8%
r 5940
11.8%
b 3240
 
6.4%
i 2646
 
5.2%
u 2592
 
5.1%
a 1998
 
4.0%
J 1971
 
3.9%
t 1944
 
3.9%
m 1944
 
3.9%
Other values (15) 13743
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50463
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 8505
16.9%
e 5940
11.8%
r 5940
11.8%
b 3240
 
6.4%
i 2646
 
5.2%
u 2592
 
5.1%
a 1998
 
4.0%
J 1971
 
3.9%
t 1944
 
3.9%
m 1944
 
3.9%
Other values (15) 13743
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50463
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 8505
16.9%
e 5940
11.8%
r 5940
11.8%
b 3240
 
6.4%
i 2646
 
5.2%
u 2592
 
5.1%
a 1998
 
4.0%
J 1971
 
3.9%
t 1944
 
3.9%
m 1944
 
3.9%
Other values (15) 13743
27.2%

UF
Categorical

UNIFORM 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size61.5 KiB
AC
 
291
AL
 
291
AM
 
291
AP
 
291
BA
 
291
Other values (22)
6402 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters15714
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAC
2nd rowAL
3rd rowAM
4th rowAP
5th rowBA

Common Values

ValueCountFrequency (%)
AC 291
 
3.7%
AL 291
 
3.7%
AM 291
 
3.7%
AP 291
 
3.7%
BA 291
 
3.7%
CE 291
 
3.7%
DF 291
 
3.7%
ES 291
 
3.7%
GO 291
 
3.7%
MA 291
 
3.7%
Other values (17) 4947
63.0%

Length

2024-06-03T21:59:11.421451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ac 291
 
3.7%
pb 291
 
3.7%
sp 291
 
3.7%
se 291
 
3.7%
sc 291
 
3.7%
rs 291
 
3.7%
rr 291
 
3.7%
ro 291
 
3.7%
rn 291
 
3.7%
rj 291
 
3.7%
Other values (17) 4947
63.0%

Most occurring characters

ValueCountFrequency (%)
A 2037
13.0%
P 2037
13.0%
R 2037
13.0%
S 1746
11.1%
M 1455
9.3%
E 1164
7.4%
C 873
 
5.6%
O 873
 
5.6%
B 582
 
3.7%
G 582
 
3.7%
Other values (7) 2328
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15714
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2037
13.0%
P 2037
13.0%
R 2037
13.0%
S 1746
11.1%
M 1455
9.3%
E 1164
7.4%
C 873
 
5.6%
O 873
 
5.6%
B 582
 
3.7%
G 582
 
3.7%
Other values (7) 2328
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15714
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2037
13.0%
P 2037
13.0%
R 2037
13.0%
S 1746
11.1%
M 1455
9.3%
E 1164
7.4%
C 873
 
5.6%
O 873
 
5.6%
B 582
 
3.7%
G 582
 
3.7%
Other values (7) 2328
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15714
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2037
13.0%
P 2037
13.0%
R 2037
13.0%
S 1746
11.1%
M 1455
9.3%
E 1164
7.4%
C 873
 
5.6%
O 873
 
5.6%
B 582
 
3.7%
G 582
 
3.7%
Other values (7) 2328
14.8%

IMPOSTO SOBRE IMPORTAÇÃO
Real number (ℝ)

MISSING 

Distinct7548
Distinct (%)99.6%
Missing281
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean88431699
Minimum-591595
Maximum2.7952883 × 109
Zeros23
Zeros (%)0.3%
Negative4
Negative (%)0.1%
Memory size61.5 KiB
2024-06-03T21:59:11.467430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-591595
5-th percentile1183.395
Q1145556.57
median4568056.9
Q361088941
95-th percentile3.9737359 × 108
Maximum2.7952883 × 109
Range2.7958799 × 109
Interquartile range (IQR)60943385

Descriptive statistics

Standard deviation2.6025181 × 108
Coefficient of variation (CV)2.9429696
Kurtosis33.244695
Mean88431699
Median Absolute Deviation (MAD)4566349.8
Skewness5.3707942
Sum6.6995855 × 1011
Variance6.7731002 × 1016
MonotonicityNot monotonic
2024-06-03T21:59:11.522653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
 
0.3%
272 2
 
< 0.1%
22 2
 
< 0.1%
158 2
 
< 0.1%
346754 2
 
< 0.1%
0.27 2
 
< 0.1%
94 2
 
< 0.1%
4815182.95 1
 
< 0.1%
1407756.84 1
 
< 0.1%
5684125.95 1
 
< 0.1%
Other values (7538) 7538
95.9%
(Missing) 281
 
3.6%
ValueCountFrequency (%)
-591595 1
 
< 0.1%
-179256.56 1
 
< 0.1%
-5357 1
 
< 0.1%
-3672 1
 
< 0.1%
0 23
0.3%
0.18 1
 
< 0.1%
0.24 1
 
< 0.1%
0.27 2
 
< 0.1%
0.35 1
 
< 0.1%
0.52 1
 
< 0.1%
ValueCountFrequency (%)
2795288310 1
< 0.1%
2576983664 1
< 0.1%
2486881748 1
< 0.1%
2448990126 1
< 0.1%
2432077264 1
< 0.1%
2418310416 1
< 0.1%
2416954418 1
< 0.1%
2404819436 1
< 0.1%
2365364831 1
< 0.1%
2352186885 1
< 0.1%

IMPOSTO SOBRE EXPORTAÇÃO
Real number (ℝ)

SKEWED  ZEROS 

Distinct6125
Distinct (%)78.4%
Missing45
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean641036.35
Minimum-3379762
Maximum1.2069438 × 109
Zeros912
Zeros (%)11.6%
Negative1770
Negative (%)22.5%
Memory size61.5 KiB
2024-06-03T21:59:11.580504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3379762
5-th percentile-224.2655
Q10
median324.325
Q38588.2675
95-th percentile409031.87
Maximum1.2069438 × 109
Range1.2103235 × 109
Interquartile range (IQR)8588.2675

Descriptive statistics

Standard deviation21717242
Coefficient of variation (CV)33.878332
Kurtosis2140.7101
Mean641036.35
Median Absolute Deviation (MAD)458.47
Skewness45.145077
Sum5.007776 × 109
Variance4.7163862 × 1014
MonotonicityNot monotonic
2024-06-03T21:59:11.640366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 912
 
11.6%
10 39
 
0.5%
13 23
 
0.3%
12 21
 
0.3%
-1 20
 
0.3%
-2 18
 
0.2%
14 16
 
0.2%
15 14
 
0.2%
18 11
 
0.1%
17 10
 
0.1%
Other values (6115) 6728
85.6%
(Missing) 45
 
0.6%
ValueCountFrequency (%)
-3379762 1
< 0.1%
-915595.88 1
< 0.1%
-843667 1
< 0.1%
-810594.46 1
< 0.1%
-509835 1
< 0.1%
-167979.53 1
< 0.1%
-153868.69 1
< 0.1%
-151357.41 1
< 0.1%
-138164.3 1
< 0.1%
-103932.27 1
< 0.1%
ValueCountFrequency (%)
1206943782 1
< 0.1%
917581499.6 1
< 0.1%
899717250.1 1
< 0.1%
701867577 1
< 0.1%
202101249.1 1
< 0.1%
133775906.7 1
< 0.1%
114082348.4 1
< 0.1%
99657204.88 1
< 0.1%
31327267.91 1
< 0.1%
30684772.7 1
< 0.1%

IPI - FUMO
Real number (ℝ)

MISSING  ZEROS 

Distinct4520
Distinct (%)59.7%
Missing292
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean12317264
Minimum-43291858
Maximum8.9447763 × 108
Zeros2944
Zeros (%)37.5%
Negative6
Negative (%)0.1%
Memory size61.5 KiB
2024-06-03T21:59:11.698386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-43291858
5-th percentile0
Q10
median20123.56
Q31744235
95-th percentile58609830
Maximum8.9447763 × 108
Range9.3776949 × 108
Interquartile range (IQR)1744235

Descriptive statistics

Standard deviation52847563
Coefficient of variation (CV)4.2905278
Kurtosis54.363738
Mean12317264
Median Absolute Deviation (MAD)20123.56
Skewness6.643386
Sum9.31801 × 1010
Variance2.7928649 × 1015
MonotonicityNot monotonic
2024-06-03T21:59:11.754134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2944
37.5%
60.11 34
 
0.4%
320.69 12
 
0.2%
7.26 7
 
0.1%
4560 5
 
0.1%
182.81 5
 
0.1%
6840 5
 
0.1%
100 5
 
0.1%
840 4
 
0.1%
337.7 4
 
0.1%
Other values (4510) 4540
57.8%
(Missing) 292
 
3.7%
ValueCountFrequency (%)
-43291858.2 1
 
< 0.1%
-16632674 1
 
< 0.1%
-1023914 1
 
< 0.1%
-797349 1
 
< 0.1%
-680 1
 
< 0.1%
-393.71 1
 
< 0.1%
0 2944
37.5%
2.99 1
 
< 0.1%
7.26 7
 
0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
894477627 1
< 0.1%
726381115 1
< 0.1%
660450316.7 1
< 0.1%
653634002 1
< 0.1%
575289369 1
< 0.1%
568819463.4 1
< 0.1%
564981441 1
< 0.1%
533130476.6 1
< 0.1%
521846903.4 1
< 0.1%
516544807.5 1
< 0.1%

IPI - BEBIDAS
Real number (ℝ)

MISSING  ZEROS 

Distinct6899
Distinct (%)91.2%
Missing292
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean7692382.6
Minimum-10797231
Maximum1.4564511 × 108
Zeros664
Zeros (%)8.5%
Negative3
Negative (%)< 0.1%
Memory size61.5 KiB
2024-06-03T21:59:11.807630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-10797231
5-th percentile0
Q1358145.46
median2732534
Q38547034.5
95-th percentile32490411
Maximum1.4564511 × 108
Range1.5644234 × 108
Interquartile range (IQR)8188889

Descriptive statistics

Standard deviation14162902
Coefficient of variation (CV)1.8411594
Kurtosis18.548494
Mean7692382.6
Median Absolute Deviation (MAD)2709879
Skewness3.8486976
Sum5.8192874 × 1010
Variance2.005878 × 1014
MonotonicityNot monotonic
2024-06-03T21:59:11.866305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 664
 
8.5%
3557 2
 
< 0.1%
823 2
 
< 0.1%
780 2
 
< 0.1%
5226439.3 1
 
< 0.1%
6557805.46 1
 
< 0.1%
1342702.29 1
 
< 0.1%
22954960.3 1
 
< 0.1%
2927592.59 1
 
< 0.1%
1745181.36 1
 
< 0.1%
Other values (6889) 6889
87.7%
(Missing) 292
 
3.7%
ValueCountFrequency (%)
-10797231 1
 
< 0.1%
-3797536 1
 
< 0.1%
-95.01 1
 
< 0.1%
0 664
8.5%
1.599 1
 
< 0.1%
3.188 1
 
< 0.1%
3.36 1
 
< 0.1%
6.187 1
 
< 0.1%
7.559 1
 
< 0.1%
9.184 1
 
< 0.1%
ValueCountFrequency (%)
145645112.6 1
< 0.1%
133996570 1
< 0.1%
133604881.2 1
< 0.1%
132778835.4 1
< 0.1%
125217654 1
< 0.1%
115006780.9 1
< 0.1%
112619720 1
< 0.1%
110193285.4 1
< 0.1%
108822729 1
< 0.1%
108105884 1
< 0.1%

IPI - AUTOMÓVEIS
Real number (ℝ)

MISSING  ZEROS 

Distinct4186
Distinct (%)54.1%
Missing115
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean11400881
Minimum-5424603.3
Maximum3.4766857 × 108
Zeros3309
Zeros (%)42.1%
Negative7
Negative (%)0.1%
Memory size61.5 KiB
2024-06-03T21:59:11.925242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-5424603.3
5-th percentile0
Q10
median2374.14
Q3653390.38
95-th percentile67275009
Maximum3.4766857 × 108
Range3.5309317 × 108
Interquartile range (IQR)653390.38

Descriptive statistics

Standard deviation34255107
Coefficient of variation (CV)3.0046018
Kurtosis22.584024
Mean11400881
Median Absolute Deviation (MAD)2374.14
Skewness4.4352855
Sum8.826562 × 1010
Variance1.1734124 × 1015
MonotonicityNot monotonic
2024-06-03T21:59:11.984583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3309
42.1%
155 24
 
0.3%
115 24
 
0.3%
60.04 18
 
0.2%
30 15
 
0.2%
71 14
 
0.2%
253.04 13
 
0.2%
75 10
 
0.1%
6385.47 10
 
0.1%
25239.36 10
 
0.1%
Other values (4176) 4295
54.7%
(Missing) 115
 
1.5%
ValueCountFrequency (%)
-5424603.3 1
 
< 0.1%
-1454896.09 1
 
< 0.1%
-683502 1
 
< 0.1%
-538308.36 1
 
< 0.1%
-223003 1
 
< 0.1%
-6484 1
 
< 0.1%
-1977.37 1
 
< 0.1%
0 3309
42.1%
1.066 1
 
< 0.1%
1.126 1
 
< 0.1%
ValueCountFrequency (%)
347668565.9 1
< 0.1%
346669851.7 1
< 0.1%
288739237 1
< 0.1%
287028935.3 1
< 0.1%
276687114.8 1
< 0.1%
273868775.4 1
< 0.1%
272996515.7 1
< 0.1%
271900897.4 1
< 0.1%
268493463 1
< 0.1%
264567969.7 1
< 0.1%

IPI - VINCULADO À IMPORTACAO
Real number (ℝ)

MISSING  ZEROS 

Distinct7011
Distinct (%)92.0%
Missing239
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean41207966
Minimum0
Maximum1.1521469 × 109
Zeros542
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size61.5 KiB
2024-06-03T21:59:12.043618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q190840.805
median1768570.1
Q333349580
95-th percentile1.9079012 × 108
Maximum1.1521469 × 109
Range1.1521469 × 109
Interquartile range (IQR)33258739

Descriptive statistics

Standard deviation1.0492993 × 108
Coefficient of variation (CV)2.5463506
Kurtosis26.986577
Mean41207966
Median Absolute Deviation (MAD)1768570.1
Skewness4.6802597
Sum3.1392229 × 1011
Variance1.101029 × 1016
MonotonicityNot monotonic
2024-06-03T21:59:12.101631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 542
 
6.9%
918.23 19
 
0.2%
12968.03 14
 
0.2%
4328.32 8
 
0.1%
437.99 6
 
0.1%
10 5
 
0.1%
2018.65 3
 
< 0.1%
690 2
 
< 0.1%
717 2
 
< 0.1%
8351 2
 
< 0.1%
Other values (7001) 7015
89.3%
(Missing) 239
 
3.0%
ValueCountFrequency (%)
0 542
6.9%
1.051 1
 
< 0.1%
1.112 1
 
< 0.1%
1.248 1
 
< 0.1%
1.252 1
 
< 0.1%
1.573 1
 
< 0.1%
1.807 1
 
< 0.1%
1.913 1
 
< 0.1%
2.024 1
 
< 0.1%
2.165 1
 
< 0.1%
ValueCountFrequency (%)
1152146874 1
< 0.1%
1091502050 1
< 0.1%
1071842066 1
< 0.1%
1033096749 1
< 0.1%
992679037.3 1
< 0.1%
981389621.3 1
< 0.1%
978509769.7 1
< 0.1%
975560711.8 1
< 0.1%
954352343.1 1
< 0.1%
948964700 1
< 0.1%

IPI - OUTROS
Real number (ℝ)

MISSING 

Distinct7594
Distinct (%)> 99.9%
Missing261
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean57246258
Minimum-3485777.1
Maximum1.6660526 × 109
Zeros1
Zeros (%)< 0.1%
Negative7
Negative (%)0.1%
Memory size61.5 KiB
2024-06-03T21:59:12.157158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3485777.1
5-th percentile12567.355
Q11293855
median4220059
Q342230421
95-th percentile2.0717293 × 108
Maximum1.6660526 × 109
Range1.6695384 × 109
Interquartile range (IQR)40936566

Descriptive statistics

Standard deviation1.6389173 × 108
Coefficient of variation (CV)2.8629247
Kurtosis28.727789
Mean57246258
Median Absolute Deviation (MAD)4156258.7
Skewness5.1056105
Sum4.3484258 × 1011
Variance2.6860499 × 1016
MonotonicityNot monotonic
2024-06-03T21:59:12.213541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5572 2
 
< 0.1%
9050 2
 
< 0.1%
1558 1
 
< 0.1%
1772492.39 1
 
< 0.1%
38651422.72 1
 
< 0.1%
2337664.03 1
 
< 0.1%
2856667.48 1
 
< 0.1%
3655436.49 1
 
< 0.1%
5822596.94 1
 
< 0.1%
108763890.7 1
 
< 0.1%
Other values (7584) 7584
96.5%
(Missing) 261
 
3.3%
ValueCountFrequency (%)
-3485777.06 1
< 0.1%
-3264033 1
< 0.1%
-3067198.52 1
< 0.1%
-2291920.37 1
< 0.1%
-34206 1
< 0.1%
-7871 1
< 0.1%
-4169.42 1
< 0.1%
0 1
< 0.1%
2.396 1
< 0.1%
2.912 1
< 0.1%
ValueCountFrequency (%)
1666052600 1
< 0.1%
1654772393 1
< 0.1%
1583899038 1
< 0.1%
1520479816 1
< 0.1%
1482288138 1
< 0.1%
1361877106 1
< 0.1%
1357775562 1
< 0.1%
1349707454 1
< 0.1%
1320541053 1
< 0.1%
1293717225 1
< 0.1%

IRPF
Real number (ℝ)

MISSING 

Distinct7553
Distinct (%)100.0%
Missing304
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean76686948
Minimum103.641
Maximum5.2992448 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.5 KiB
2024-06-03T21:59:12.272325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum103.641
5-th percentile598220.03
Q14329092.4
median15607038
Q351837837
95-th percentile3.477275 × 108
Maximum5.2992448 × 109
Range5.2992447 × 109
Interquartile range (IQR)47508745

Descriptive statistics

Standard deviation2.2571401 × 108
Coefficient of variation (CV)2.9433171
Kurtosis118.24585
Mean76686948
Median Absolute Deviation (MAD)13629593
Skewness8.6104716
Sum5.7921652 × 1011
Variance5.0946813 × 1016
MonotonicityNot monotonic
2024-06-03T21:59:12.332372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177197 1
 
< 0.1%
143650607.8 1
 
< 0.1%
20757719.92 1
 
< 0.1%
16146287.78 1
 
< 0.1%
5321333.06 1
 
< 0.1%
7820312.71 1
 
< 0.1%
838414188.1 1
 
< 0.1%
19516324.91 1
 
< 0.1%
80515724.97 1
 
< 0.1%
205057629.5 1
 
< 0.1%
Other values (7543) 7543
96.0%
(Missing) 304
 
3.9%
ValueCountFrequency (%)
103.641 1
< 0.1%
110.331 1
< 0.1%
119.295 1
< 0.1%
120.157 1
< 0.1%
122.83 1
< 0.1%
148.235 1
< 0.1%
150.887 1
< 0.1%
152.11 1
< 0.1%
153.08 1
< 0.1%
155.996 1
< 0.1%
ValueCountFrequency (%)
5299244801 1
< 0.1%
4806777728 1
< 0.1%
4083826684 1
< 0.1%
3459117027 1
< 0.1%
3197791235 1
< 0.1%
3168603820 1
< 0.1%
2942153033 1
< 0.1%
2719045366 1
< 0.1%
2622912827 1
< 0.1%
2385029848 1
< 0.1%

IRPJ - ENTIDADES FINANCEIRAS
Real number (ℝ)

MISSING  ZEROS 

Distinct6971
Distinct (%)90.4%
Missing147
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean48166957
Minimum-8099952.9
Maximum7.7296189 × 109
Zeros655
Zeros (%)8.3%
Negative55
Negative (%)0.7%
Memory size61.5 KiB
2024-06-03T21:59:12.392356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-8099952.9
5-th percentile0
Q13170.4775
median234961.21
Q38159727.3
95-th percentile2.3764704 × 108
Maximum7.7296189 × 109
Range7.7377188 × 109
Interquartile range (IQR)8156556.8

Descriptive statistics

Standard deviation2.3131119 × 108
Coefficient of variation (CV)4.8022794
Kurtosis256.98926
Mean48166957
Median Absolute Deviation (MAD)234961.21
Skewness12.521307
Sum3.7136724 × 1011
Variance5.3504866 × 1016
MonotonicityNot monotonic
2024-06-03T21:59:12.453418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 655
 
8.3%
77 4
 
0.1%
169 3
 
< 0.1%
1517 3
 
< 0.1%
123 3
 
< 0.1%
13 3
 
< 0.1%
591 3
 
< 0.1%
1374 2
 
< 0.1%
281 2
 
< 0.1%
522 2
 
< 0.1%
Other values (6961) 7030
89.5%
(Missing) 147
 
1.9%
ValueCountFrequency (%)
-8099952.92 1
< 0.1%
-3140682 1
< 0.1%
-2263798.08 1
< 0.1%
-768076.84 1
< 0.1%
-470737 1
< 0.1%
-306415.73 1
< 0.1%
-222111 1
< 0.1%
-201909 1
< 0.1%
-198388.82 1
< 0.1%
-194368.49 1
< 0.1%
ValueCountFrequency (%)
7729618874 1
< 0.1%
4866718247 1
< 0.1%
3965647816 1
< 0.1%
3894755872 1
< 0.1%
3866010419 1
< 0.1%
3747430136 1
< 0.1%
3632980142 1
< 0.1%
2968318005 1
< 0.1%
2939585625 1
< 0.1%
2837593862 1
< 0.1%

IRPJ - DEMAIS EMPRESAS
Real number (ℝ)

MISSING 

Distinct7476
Distinct (%)100.0%
Missing381
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean3.2612684 × 108
Minimum303.76
Maximum2.2938669 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.5 KiB
2024-06-03T21:59:12.510410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum303.76
5-th percentile2042141.2
Q112262235
median46923119
Q31.9860153 × 108
95-th percentile1.5427118 × 109
Maximum2.2938669 × 1010
Range2.2938669 × 1010
Interquartile range (IQR)1.863393 × 108

Descriptive statistics

Standard deviation1.0410545 × 109
Coefficient of variation (CV)3.1921767
Kurtosis97.265925
Mean3.2612684 × 108
Median Absolute Deviation (MAD)42223059
Skewness8.1251152
Sum2.4381243 × 1012
Variance1.0837945 × 1018
MonotonicityNot monotonic
2024-06-03T21:59:12.565643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
676835 1
 
< 0.1%
206838444.1 1
 
< 0.1%
4128814.1 1
 
< 0.1%
32492048.14 1
 
< 0.1%
5088344209 1
 
< 0.1%
28125765.27 1
 
< 0.1%
476147889.8 1
 
< 0.1%
677504211.1 1
 
< 0.1%
9327986.19 1
 
< 0.1%
31716359.33 1
 
< 0.1%
Other values (7466) 7466
95.0%
(Missing) 381
 
4.8%
ValueCountFrequency (%)
303.76 1
< 0.1%
335.28 1
< 0.1%
352.193 1
< 0.1%
410.47 1
< 0.1%
440.441 1
< 0.1%
446.105 1
< 0.1%
452.391 1
< 0.1%
454.62 1
< 0.1%
465.803 1
< 0.1%
468.565 1
< 0.1%
ValueCountFrequency (%)
2.293866899 × 10101
< 0.1%
1.83297883 × 10101
< 0.1%
1.658856326 × 10101
< 0.1%
1.586695916 × 10101
< 0.1%
1.47341015 × 10101
< 0.1%
1.375038614 × 10101
< 0.1%
1.3150664 × 10101
< 0.1%
1.2750939 × 10101
< 0.1%
1.224592339 × 10101
< 0.1%
1.209284469 × 10101
< 0.1%

IRRF - RENDIMENTOS DO TRABALHO
Real number (ℝ)

MISSING 

Distinct7482
Distinct (%)100.0%
Missing375
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean2.5676368 × 108
Minimum-74712501
Maximum9.7563497 × 109
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)< 0.1%
Memory size61.5 KiB
2024-06-03T21:59:12.622233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-74712501
5-th percentile2560261.4
Q111050708
median36344892
Q31.275928 × 108
95-th percentile1.4377334 × 109
Maximum9.7563497 × 109
Range9.8310622 × 109
Interquartile range (IQR)1.165421 × 108

Descriptive statistics

Standard deviation6.9532432 × 108
Coefficient of variation (CV)2.7080322
Kurtosis30.757717
Mean2.5676368 × 108
Median Absolute Deviation (MAD)30760948
Skewness4.8964272
Sum1.9211059 × 1012
Variance4.8347591 × 1017
MonotonicityNot monotonic
2024-06-03T21:59:12.680383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1084940 1
 
< 0.1%
51168106.39 1
 
< 0.1%
23481693.07 1
 
< 0.1%
157336093.4 1
 
< 0.1%
290217608.3 1
 
< 0.1%
5760825.93 1
 
< 0.1%
15572076.89 1
 
< 0.1%
34131349.54 1
 
< 0.1%
963443716.8 1
 
< 0.1%
250791032.5 1
 
< 0.1%
Other values (7472) 7472
95.1%
(Missing) 375
 
4.8%
ValueCountFrequency (%)
-74712501 1
< 0.1%
-13309526 1
< 0.1%
-130560 1
< 0.1%
323.793 1
< 0.1%
349.434 1
< 0.1%
352.106 1
< 0.1%
383.444 1
< 0.1%
392.2 1
< 0.1%
393.59 1
< 0.1%
394.214 1
< 0.1%
ValueCountFrequency (%)
9756349736 1
< 0.1%
8648871034 1
< 0.1%
7928286209 1
< 0.1%
7309523966 1
< 0.1%
7059791297 1
< 0.1%
6900785699 1
< 0.1%
6680725125 1
< 0.1%
5805238472 1
< 0.1%
5692942112 1
< 0.1%
5614131188 1
< 0.1%

IRRF - RENDIMENTOS DO CAPITAL
Real number (ℝ)

MISSING 

Distinct7639
Distinct (%)> 99.9%
Missing216
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean1.2474594 × 108
Minimum-1.8721384 × 108
Maximum1.4463375 × 1010
Zeros0
Zeros (%)0.0%
Negative9
Negative (%)0.1%
Memory size61.5 KiB
2024-06-03T21:59:12.738605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1.8721384 × 108
5-th percentile29041.42
Q1655085
median3405592.6
Q322732744
95-th percentile5.9329892 × 108
Maximum1.4463375 × 1010
Range1.4650589 × 1010
Interquartile range (IQR)22077659

Descriptive statistics

Standard deviation5.6371841 × 108
Coefficient of variation (CV)4.518932
Kurtosis141.83137
Mean1.2474594 × 108
Median Absolute Deviation (MAD)3293285.2
Skewness9.8727316
Sum9.5318373 × 1011
Variance3.1777845 × 1017
MonotonicityNot monotonic
2024-06-03T21:59:12.801073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16603 2
 
< 0.1%
13438 2
 
< 0.1%
4270011.43 1
 
< 0.1%
969044.08 1
 
< 0.1%
672969.46 1
 
< 0.1%
2013806310 1
 
< 0.1%
1729860.91 1
 
< 0.1%
29935792.91 1
 
< 0.1%
73292727.23 1
 
< 0.1%
170344.49 1
 
< 0.1%
Other values (7629) 7629
97.1%
(Missing) 216
 
2.7%
ValueCountFrequency (%)
-187213840.3 1
< 0.1%
-24571449 1
< 0.1%
-16711966.76 1
< 0.1%
-5810771.01 1
< 0.1%
-1282364 1
< 0.1%
-1016583 1
< 0.1%
-988882 1
< 0.1%
-142198 1
< 0.1%
-80675 1
< 0.1%
9.034 1
< 0.1%
ValueCountFrequency (%)
1.44633752 × 10101
< 0.1%
1.103565898 × 10101
< 0.1%
9357326591 1
< 0.1%
8928949421 1
< 0.1%
8596446922 1
< 0.1%
7966291318 1
< 0.1%
7381185557 1
< 0.1%
7214770855 1
< 0.1%
7153167541 1
< 0.1%
6945342239 1
< 0.1%

IRRF - REMESSAS P/ EXTERIOR
Real number (ℝ)

MISSING  ZEROS 

Distinct7460
Distinct (%)97.2%
Missing183
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean62413932
Minimum-2.59046 × 108
Maximum6.713613 × 109
Zeros205
Zeros (%)2.6%
Negative30
Negative (%)0.4%
Memory size61.5 KiB
2024-06-03T21:59:12.859884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-2.59046 × 108
5-th percentile109.8512
Q1109436.76
median1438582.4
Q311899026
95-th percentile2.8468216 × 108
Maximum6.713613 × 109
Range6.972659 × 109
Interquartile range (IQR)11789589

Descriptive statistics

Standard deviation2.9871071 × 108
Coefficient of variation (CV)4.7859621
Kurtosis132.94005
Mean62413932
Median Absolute Deviation (MAD)1437922.1
Skewness9.8871244
Sum4.7896451 × 1011
Variance8.9228089 × 1016
MonotonicityNot monotonic
2024-06-03T21:59:12.921706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 205
 
2.6%
108 3
 
< 0.1%
50 2
 
< 0.1%
5719 2
 
< 0.1%
270 2
 
< 0.1%
325 2
 
< 0.1%
3709 2
 
< 0.1%
100 2
 
< 0.1%
401 2
 
< 0.1%
107 2
 
< 0.1%
Other values (7450) 7450
94.8%
(Missing) 183
 
2.3%
ValueCountFrequency (%)
-259045996.4 1
< 0.1%
-22803113.7 1
< 0.1%
-1526698.73 1
< 0.1%
-715431 1
< 0.1%
-315331.3 1
< 0.1%
-267051 1
< 0.1%
-245886 1
< 0.1%
-127238 1
< 0.1%
-84114 1
< 0.1%
-69294 1
< 0.1%
ValueCountFrequency (%)
6713613018 1
< 0.1%
6145975184 1
< 0.1%
5207429452 1
< 0.1%
4899769770 1
< 0.1%
4846939055 1
< 0.1%
4818325178 1
< 0.1%
3906045956 1
< 0.1%
3826409099 1
< 0.1%
3741085873 1
< 0.1%
3392502290 1
< 0.1%

IRRF - OUTROS RENDIMENTOS
Real number (ℝ)

MISSING 

Distinct7638
Distinct (%)> 99.9%
Missing217
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean26358334
Minimum-2555620
Maximum9.3380086 × 108
Zeros2
Zeros (%)< 0.1%
Negative11
Negative (%)0.1%
Memory size61.5 KiB
2024-06-03T21:59:12.983291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-2555620
5-th percentile63769
Q1757342.75
median2841787.4
Q311265947
95-th percentile1.3679513 × 108
Maximum9.3380086 × 108
Range9.3635648 × 108
Interquartile range (IQR)10508604

Descriptive statistics

Standard deviation81466296
Coefficient of variation (CV)3.0907225
Kurtosis38.769363
Mean26358334
Median Absolute Deviation (MAD)2583132.9
Skewness5.6946002
Sum2.0137767 × 1011
Variance6.6367574 × 1015
MonotonicityNot monotonic
2024-06-03T21:59:13.040811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36121 2
 
< 0.1%
0 2
 
< 0.1%
180347.62 1
 
< 0.1%
4441767.04 1
 
< 0.1%
1140984.84 1
 
< 0.1%
204895.32 1
 
< 0.1%
722967.68 1
 
< 0.1%
368914422.7 1
 
< 0.1%
1834728.54 1
 
< 0.1%
17260200.69 1
 
< 0.1%
Other values (7628) 7628
97.1%
(Missing) 217
 
2.8%
ValueCountFrequency (%)
-2555620 1
< 0.1%
-897777 1
< 0.1%
-820010 1
< 0.1%
-802388 1
< 0.1%
-411891 1
< 0.1%
-141905.21 1
< 0.1%
-82490.1 1
< 0.1%
-70488 1
< 0.1%
-32989 1
< 0.1%
-5223 1
< 0.1%
ValueCountFrequency (%)
933800860 1
< 0.1%
912120667.3 1
< 0.1%
854005675.6 1
< 0.1%
846955795.1 1
< 0.1%
820522199.4 1
< 0.1%
802941640.3 1
< 0.1%
799631949.1 1
< 0.1%
795127585.6 1
< 0.1%
791709092.9 1
< 0.1%
785582862.1 1
< 0.1%

IMPOSTO S/ OPERAÇÕES FINANCEIRAS
Real number (ℝ)

MISSING 

Distinct7627
Distinct (%)99.1%
Missing162
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean79046169
Minimum-268901
Maximum3.7634486 × 109
Zeros46
Zeros (%)0.6%
Negative6
Negative (%)0.1%
Memory size61.5 KiB
2024-06-03T21:59:13.107345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-268901
5-th percentile764.6
Q1179997.46
median1802837.4
Q311647956
95-th percentile3.4230614 × 108
Maximum3.7634486 × 109
Range3.7637175 × 109
Interquartile range (IQR)11467959

Descriptive statistics

Standard deviation3.376787 × 108
Coefficient of variation (CV)4.2719174
Kurtosis50.750475
Mean79046169
Median Absolute Deviation (MAD)1790600.4
Skewness6.6803642
Sum6.0826027 × 1011
Variance1.1402691 × 1017
MonotonicityNot monotonic
2024-06-03T21:59:13.165652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46
 
0.6%
2 4
 
0.1%
1 4
 
0.1%
6 3
 
< 0.1%
200604.38 3
 
< 0.1%
11.442 2
 
< 0.1%
4 2
 
< 0.1%
880 2
 
< 0.1%
3 2
 
< 0.1%
949 2
 
< 0.1%
Other values (7617) 7625
97.0%
(Missing) 162
 
2.1%
ValueCountFrequency (%)
-268901 1
 
< 0.1%
-264290 1
 
< 0.1%
-181961 1
 
< 0.1%
-123276 1
 
< 0.1%
-34299 1
 
< 0.1%
-242 1
 
< 0.1%
0 46
0.6%
0.73 1
 
< 0.1%
1 4
 
0.1%
1.157 1
 
< 0.1%
ValueCountFrequency (%)
3763448587 1
< 0.1%
3752569213 1
< 0.1%
3714614551 1
< 0.1%
3694651441 1
< 0.1%
3649459694 1
< 0.1%
3647427369 1
< 0.1%
3644651338 1
< 0.1%
3559174044 1
< 0.1%
3544200799 1
< 0.1%
3507794694 1
< 0.1%

IMPOSTO TERRITORIAL RURAL
Real number (ℝ)

Distinct7790
Distinct (%)99.9%
Missing61
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean3093999.1
Minimum-17756226
Maximum3.7514995 × 108
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size61.5 KiB
2024-06-03T21:59:13.221967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-17756226
5-th percentile3193.74
Q171617.61
median265568.5
Q31086750.5
95-th percentile10238048
Maximum3.7514995 × 108
Range3.9290617 × 108
Interquartile range (IQR)1015132.9

Descriptive statistics

Standard deviation15946478
Coefficient of variation (CV)5.1540022
Kurtosis174.82286
Mean3093999.1
Median Absolute Deviation (MAD)235748.99
Skewness11.852075
Sum2.4120817 × 1010
Variance2.5429016 × 1014
MonotonicityNot monotonic
2024-06-03T21:59:13.280098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45571 2
 
< 0.1%
80256 2
 
< 0.1%
1257459 2
 
< 0.1%
59130.44 2
 
< 0.1%
20272 2
 
< 0.1%
34926 2
 
< 0.1%
28447 1
 
< 0.1%
1793420.55 1
 
< 0.1%
1384291.84 1
 
< 0.1%
297597.94 1
 
< 0.1%
Other values (7780) 7780
99.0%
(Missing) 61
 
0.8%
ValueCountFrequency (%)
-17756225.7 1
< 0.1%
-378931.78 1
< 0.1%
2.534 1
< 0.1%
3.686 1
< 0.1%
3.812 1
< 0.1%
4.109 1
< 0.1%
5.004 1
< 0.1%
5.33 1
< 0.1%
5.792 1
< 0.1%
5.829 1
< 0.1%
ValueCountFrequency (%)
375149946.2 1
< 0.1%
326509783.3 1
< 0.1%
278534088.6 1
< 0.1%
278195244.6 1
< 0.1%
265854569.2 1
< 0.1%
265208783.9 1
< 0.1%
257050943.5 1
< 0.1%
241187976 1
< 0.1%
240635534.8 1
< 0.1%
234379972.6 1
< 0.1%

CPMF
Real number (ℝ)

MISSING  ZEROS 

Distinct3666
Distinct (%)48.1%
Missing238
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean22168146
Minimum-3496452
Maximum2.332493 × 109
Zeros3917
Zeros (%)49.9%
Negative21
Negative (%)0.3%
Memory size61.5 KiB
2024-06-03T21:59:13.337892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3496452
5-th percentile0
Q10
median0
Q3113768.36
95-th percentile28614682
Maximum2.332493 × 109
Range2.3359895 × 109
Interquartile range (IQR)113768.36

Descriptive statistics

Standard deviation1.5370939 × 108
Coefficient of variation (CV)6.9337955
Kurtosis91.947543
Mean22168146
Median Absolute Deviation (MAD)0
Skewness9.1642654
Sum1.688991 × 1011
Variance2.3626576 × 1016
MonotonicityNot monotonic
2024-06-03T21:59:13.395805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3917
49.9%
2 5
 
0.1%
3 5
 
0.1%
13 4
 
0.1%
1 4
 
0.1%
4 3
 
< 0.1%
11 3
 
< 0.1%
1.03 2
 
< 0.1%
2.2 2
 
< 0.1%
7 2
 
< 0.1%
Other values (3656) 3672
46.7%
(Missing) 238
 
3.0%
ValueCountFrequency (%)
-3496452 1
< 0.1%
-689387.77 1
< 0.1%
-539556.07 1
< 0.1%
-462872.33 1
< 0.1%
-93831 1
< 0.1%
-19349 1
< 0.1%
-6724.97 1
< 0.1%
-6246.26 1
< 0.1%
-4589 1
< 0.1%
-4159.58 1
< 0.1%
ValueCountFrequency (%)
2332493003 1
< 0.1%
2115947222 1
< 0.1%
1963922063 1
< 0.1%
1955364442 1
< 0.1%
1954329648 1
< 0.1%
1951592433 1
< 0.1%
1919379762 1
< 0.1%
1906244544 1
< 0.1%
1891848620 1
< 0.1%
1886036977 1
< 0.1%

COFINS
Real number (ℝ)

MISSING  ZEROS 

Distinct865
Distinct (%)11.7%
Missing452
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean13870757
Minimum0
Maximum2.2990327 × 109
Zeros6541
Zeros (%)83.3%
Negative0
Negative (%)0.0%
Memory size61.5 KiB
2024-06-03T21:59:13.450643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile44282787
Maximum2.2990327 × 109
Range2.2990327 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.06352 × 108
Coefficient of variation (CV)7.6673538
Kurtosis218.95912
Mean13870757
Median Absolute Deviation (MAD)0
Skewness13.762957
Sum1.0271296 × 1011
Variance1.1310749 × 1016
MonotonicityNot monotonic
2024-06-03T21:59:13.508442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6541
83.3%
23172078 1
 
< 0.1%
77483367 1
 
< 0.1%
82672759 1
 
< 0.1%
41700784 1
 
< 0.1%
207662579 1
 
< 0.1%
39593451 1
 
< 0.1%
44259039 1
 
< 0.1%
10927270 1
 
< 0.1%
16987432 1
 
< 0.1%
Other values (855) 855
 
10.9%
(Missing) 452
 
5.8%
ValueCountFrequency (%)
0 6541
83.3%
1049745 1
 
< 0.1%
1104880 1
 
< 0.1%
1182289 1
 
< 0.1%
1184166 1
 
< 0.1%
1235857 1
 
< 0.1%
1245292 1
 
< 0.1%
1264476 1
 
< 0.1%
1265317 1
 
< 0.1%
1276739 1
 
< 0.1%
ValueCountFrequency (%)
2299032749 1
< 0.1%
2172645500 1
< 0.1%
2082164034 1
< 0.1%
1982544199 1
< 0.1%
1963930514 1
< 0.1%
1854137053 1
< 0.1%
1824938828 1
< 0.1%
1798997045 1
< 0.1%
1766307245 1
< 0.1%
1742924592 1
< 0.1%

TOTAL_ARRECADADO
Real number (ℝ)

Distinct7829
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2104751 × 109
Minimum-4598430
Maximum5.9264196 × 1010
Zeros29
Zeros (%)0.4%
Negative1
Negative (%)< 0.1%
Memory size61.5 KiB
2024-06-03T21:59:13.564026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-4598430
5-th percentile3799.676
Q134753541
median1.4998012 × 108
Q36.9025834 × 108
95-th percentile5.7190274 × 109
Maximum5.9264196 × 1010
Range5.9268794 × 1010
Interquartile range (IQR)6.555048 × 108

Descriptive statistics

Standard deviation3.5391607 × 109
Coefficient of variation (CV)2.9237782
Kurtosis50.772997
Mean1.2104751 × 109
Median Absolute Deviation (MAD)1.39115 × 108
Skewness6.1959104
Sum9.5107028 × 1012
Variance1.2525659 × 1019
MonotonicityNot monotonic
2024-06-03T21:59:13.623441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
0.4%
4093028 1
 
< 0.1%
3924383068 1
 
< 0.1%
4583221367 1
 
< 0.1%
1718700331 1
 
< 0.1%
48432133.44 1
 
< 0.1%
452019764.3 1
 
< 0.1%
82652084.95 1
 
< 0.1%
242814680.6 1
 
< 0.1%
147848833.8 1
 
< 0.1%
Other values (7819) 7819
99.5%
ValueCountFrequency (%)
-4598430 1
 
< 0.1%
0 29
0.4%
42.19 1
 
< 0.1%
77.554 1
 
< 0.1%
95.048 1
 
< 0.1%
102.958 1
 
< 0.1%
103.532 1
 
< 0.1%
111.12 1
 
< 0.1%
123.968 1
 
< 0.1%
141.051 1
 
< 0.1%
ValueCountFrequency (%)
5.926419576 × 10101
< 0.1%
4.977299346 × 10101
< 0.1%
4.343079737 × 10101
< 0.1%
4.241576987 × 10101
< 0.1%
3.975068897 × 10101
< 0.1%
3.872979259 × 10101
< 0.1%
3.795893113 × 10101
< 0.1%
3.661483248 × 10101
< 0.1%
3.622393744 × 10101
< 0.1%
3.609743056 × 10101
< 0.1%

Interactions

2024-06-03T21:59:09.696458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:51.346131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:52.268708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:53.167492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:54.756127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:55.733307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:56.739144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:57.613016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:58.588173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:59.437196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:00.382696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:01.387778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:02.260820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:03.330928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:04.285902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:05.225697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:06.070805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:07.125634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:08.033027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:08.855613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:09.737955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:51.404632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:52.309107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:53.209818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:54.826500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:55.774732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:56.780771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:57.654057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:58.629717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:59.476442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:00.425702image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:01.429631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:02.302990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:03.395549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:04.328969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:05.265737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:06.113136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:07.168082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:08.072103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:08.893890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:09.778942image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:51.469045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:52.412077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:53.258422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:54.885202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:55.817007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:56.824031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:57.696863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:58.671208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:59.516436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:00.470255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:01.471062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:02.346276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:03.444402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:04.371972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:05.307404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:06.156225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:07.210199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:08.113282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:08.948682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:09.822201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:51.537579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:52.454028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:53.301534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:54.968501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:55.976454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:56.867493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:57.844652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:58.714905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:59.557841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:00.519007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:01.514223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:02.389240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:03.495319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:04.421948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:05.349682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:06.200472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:07.254232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:08.155273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:08.991398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:09.862523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:51.604186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:52.494562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:53.341150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:55.013671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:56.017202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:56.908051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:57.884453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:58.753669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:59.601524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:00.581665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-06-03T21:59:02.896767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:04.018796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:04.958933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:05.821917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:06.861649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:07.770882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:08.609591image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:09.450527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:10.341082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:52.064308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:52.960897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:54.352132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:55.533728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:56.526392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:57.396750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:58.364796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:59.225094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:00.173361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:01.167395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:02.034796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:02.937093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:04.062031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:04.999336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:05.861692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:06.909588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:07.814977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:08.647377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:09.489436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:10.390391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:52.109079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:53.004846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:54.458170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:55.576501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:56.571628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:57.441655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:58.413760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:59.269974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:00.220064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:01.216910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:02.081137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:03.141625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:04.110938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:05.045811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:05.905961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:06.955620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:07.862877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:08.698876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:09.535368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:10.435352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:52.151651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:53.048449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:54.537445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:55.619010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:56.615871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:57.485839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:58.459089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:59.314481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:00.264818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:01.263134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:02.128511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:03.194044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:04.155978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:05.092412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:05.949565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:07.001979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:07.907998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:08.741873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:09.579150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:10.475149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:52.189928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:53.086844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:54.643490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:55.655663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:56.656596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:57.525395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:58.498409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:59.354576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:00.302498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:01.303819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:02.178198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:03.240489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:04.198578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:05.134966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:05.989546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:07.041955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:07.949038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:08.778329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:09.617229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:10.520168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:52.227891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:53.125873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:54.695418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:55.693077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:56.696227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:57.566062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:58.540992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:58:59.394453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:00.341349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:01.344772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:02.216996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:03.282759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:04.240156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:05.179459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:06.028949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:07.082511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:07.990585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:08.815198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-03T21:59:09.654637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-06-03T21:59:10.593447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-03T21:59:10.725380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-03T21:59:11.120469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AnoMêsUFIMPOSTO SOBRE IMPORTAÇÃOIMPOSTO SOBRE EXPORTAÇÃOIPI - FUMOIPI - BEBIDASIPI - AUTOMÓVEISIPI - VINCULADO À IMPORTACAOIPI - OUTROSIRPFIRPJ - ENTIDADES FINANCEIRASIRPJ - DEMAIS EMPRESASIRRF - RENDIMENTOS DO TRABALHOIRRF - RENDIMENTOS DO CAPITALIRRF - REMESSAS P/ EXTERIORIRRF - OUTROS RENDIMENTOSIMPOSTO S/ OPERAÇÕES FINANCEIRASIMPOSTO TERRITORIAL RURALCPMFCOFINSTOTAL_ARRECADADO
02000JaneiroAC231.00.0292096.00.00.0167.01558.0177197.0643.0676835.01084940.027652.0570.030894.01220.028447.0103.01770475.04093028.0
12000JaneiroAL475088.033873.01329338.0812470.00.0141735.03676847.0460804.0561.02672106.04454947.0544056.0191382.0309573.026939.04252539.097248.07493570.026973076.0
22000JaneiroAM11679405.00.01507146.01791471.027796.04414483.01800346.0700623.090828.010058713.010174718.01241708.03580022.01029164.0843881.040171.0832562.044000981.093814018.0
32000JaneiroAP24267.00.0424862.01419.0321.022333.012165.0139226.044135.0586572.01444486.0205031.05825.034602.06999.06477.00.01682102.04640822.0
42000JaneiroBA10476457.013.05542123.011533707.00.07496476.023743858.02498647.0177378.025695696.027277025.013374802.02302766.02022200.01218485.02446535.011923442.081027229.0228756839.0
52000JaneiroCE5576921.00.04535653.03148254.00.01734529.03716458.01971500.059874.013247447.015940490.06311419.0948600.01941958.0745659.0114393.04547426.036892799.0101433380.0
62000JaneiroDF2254341.043.03197500.02777942.00.02079272.02433339.03067374.03913461.071582680.0257391352.0124316928.07145075.035581549.022472279.064578.0240726401.0141910257.0920914371.0
72000JaneiroES31950799.00.03598736.020408.01503039.026840430.04036463.01620843.02542.029105465.09380778.010515327.04717282.01703727.0491261.01563052.028681.049157907.0176236740.0
82000JaneiroGO3747911.00.03236092.05064141.01091185.0986314.02654994.01401385.045754.018823998.013094463.06275215.0132749.01043647.0638840.02269467.03912150.047986002.0112404307.0
92000JaneiroMA1714749.069.01124738.04061569.00.0206691.0484436.0479170.04832.03251185.04149532.0781569.01130.0289120.086071.0593391.0612313.09654439.027495004.0
AnoMêsUFIMPOSTO SOBRE IMPORTAÇÃOIMPOSTO SOBRE EXPORTAÇÃOIPI - FUMOIPI - BEBIDASIPI - AUTOMÓVEISIPI - VINCULADO À IMPORTACAOIPI - OUTROSIRPFIRPJ - ENTIDADES FINANCEIRASIRPJ - DEMAIS EMPRESASIRRF - RENDIMENTOS DO TRABALHOIRRF - RENDIMENTOS DO CAPITALIRRF - REMESSAS P/ EXTERIORIRRF - OUTROS RENDIMENTOSIMPOSTO S/ OPERAÇÕES FINANCEIRASIMPOSTO TERRITORIAL RURALCPMFCOFINSTOTAL_ARRECADADO
78472024MarçoPI1.080297e+07-1.800.000000e+001710526.750.000000e+004.311223e+062.129538e+061.310838e+071.115296e+044.492228e+073.768966e+071.758334e+062.981880e+064.110460e+062.214084e+05965411.200.00.01.247232e+08
78482024MarçoRJ2.265810e+0818741.065.774848e+0510266135.403.083134e+071.012351e+081.230349e+082.230489e+082.579138e+084.913295e+092.388072e+092.632400e+096.509999e+081.581961e+082.913355e+081158120.570.00.01.200896e+10
78492024MarçoRN6.531258e+06-3.057.260000e+00418137.215.431790e+032.491648e+062.535647e+061.449881e+076.778428e+055.258138e+076.143646e+074.437379e+062.109778e+063.804874e+061.456330e+06187905.940.00.01.531729e+08
78502024MarçoRS1.198907e+082338.781.623147e+0817229136.821.669087e+071.150825e+082.306029e+081.420642e+083.163137e+076.183760e+087.526882e+081.632696e+087.807834e+075.847474e+072.025337e+083581625.900.00.02.712511e+09
78512024MarçoRO4.259459e+07-92.430.000000e+0015936.013.236959e+041.686069e+077.335857e+068.798839e+064.372943e+052.520413e+073.613082e+076.921385e+063.214756e+051.361129e+061.714452e+07886899.630.00.01.640458e+08
78522024MarçoRR4.176311e+040.000.000000e+000.000.000000e+001.313218e+048.405307e+042.931498e+063.858670e+031.035559e+071.296915e+073.032121e+052.007814e+048.286758e+051.256391e+05115602.220.00.02.779224e+07
78532024MarçoSC1.145658e+09-2721.898.276000e+0110385020.783.971773e+074.199974e+083.143088e+089.483545e+071.801126e+077.018191e+084.223393e+089.516803e+075.891456e+073.185204e+078.297521e+07951322.260.00.03.436930e+09
78542024MarçoSP1.778419e+0938203.152.386842e+0651120764.982.273373e+085.498449e+081.233497e+097.106990e+081.527903e+097.063112e+097.309524e+096.538369e+092.754760e+098.540057e+083.544201e+0918847271.310.00.03.416406e+10
78552024MarçoSE5.666047e+061872.383.309714e+051262320.150.000000e+001.161892e+068.561310e+061.039469e+072.975598e+063.226274e+073.525751e+074.082837e+061.676212e+063.330496e+065.288795e+0677990.460.00.01.123313e+08
78562024MarçoTO4.609480e+06-0.010.000000e+002070.370.000000e+001.609433e+068.335072e+056.547910e+061.875666e+044.392847e+072.059676e+077.263769e+058.403451e+051.262365e+064.524017e+051476992.190.00.08.290487e+07